CombIncrease_next {dfcomb}  R Documentation 
Combination determination with logistic model
Description
CombIncrease_next
is used to determine the next or recommended combination in a phase I combination clinical trial using the design proposed by Riviere et al. entitled "A Bayesian dosefinding design for drug combination clinical trials based on the logistic model".
Usage
CombIncrease_next(ndose_a1, ndose_a2, target, target_min, target_max,
prior_tox_a1, prior_tox_a2, cohort, final, pat_incl, dose_adm1,
dose_adm2, tite=FALSE, toxicity, time_full=0, time_tox=0,
time_follow=0, c_e=0.85, c_d=0.45, c_stop=0.95, c_t=0.5, c_over=0.25,
cmin_overunder=2, cmin_mtd=3, cmin_recom=1, early_stop=1, alloc_rule=1,
nburn=2000, niter=5000)
Arguments
ndose_a1 
Number of dose levels for agent 1. 
ndose_a2 
Number of dose levels for agent 2. 
target 
Toxicity (probability) target. 
target_min 
Minimum of the targeted toxicity interval. 
target_max 
Maximum of the targeted toxicity interval. 
prior_tox_a1 
A vector of initial guesses of toxicity probabilities associated with the doses of agent 1. Must be of length 
prior_tox_a2 
A vector of initial guesses of toxicity probabilities associated with the doses of agent 2. Must be of length 
cohort 
Cohort size. 
final 
A boolean with value TRUE if the trial is finished and the recommended combination for further phases should be given, or FALSE (default value) if the combination determination is performed for the next cohort of patients. 
pat_incl 
Current number of patients included. 
dose_adm1 
A vector indicating the dose levels of agents 1 administered to each patient included in the trial. Must be of length 
dose_adm2 
A vector indicating the dose levels of agents 2 administered to each patient included in the trial. Must be of length 
tite 
A boolean indicating if the toxicity is considered as a timetoevent outcome (TRUE), or as a binary outcome (default value FALSE). 
toxicity 
A vector of observed toxicities (DLTs) for each patient included in the trial. Must be of length 
time_full 
Full followup time window. This argument is used only if tite=TRUE. 
time_tox 
A vector of timestotoxicity for each patient included in the trial. If no toxicity was observed for a patient, must be filled with +Inf. Must be of length 
time_follow 
A vector of followup times for each patient included in the trial. Must be of length 
c_e 
Probability threshold for doseescalation. The default value is set at 0.85. 
c_d 
Probability threshold for dosedeescalation. The default value is set at 0.45. 
c_stop 
Probability threshold for early trial termination. The default value is set at 0.95. 
c_t 
Probability threshold for early trial termination for finding the MTD (see details). The default value is set at 0.5. 
c_over 
Probability threshold to control overdosing (see details). 
cmin_overunder 
Minimum number of cohorts to be included at the lowest/highest combination before possible early trial termination for overtoxicity or undertoxicity (see details). The default value is set at 2. 
cmin_mtd 
Minimum number of cohorts to be included at the recommended combination before possible early trial termination for finding the MTD (see details). The default value is set at 3. 
cmin_recom 
Minimum number of cohorts to be included at the recommended combination at the end of the trial. The default value is set at 1. 
alloc_rule 
Interger (1, 2, or 3) indicating which allocation rule is used (see details). The default value is set at 1. 
early_stop 
Interger (1, 2, or 3) indicating which early stopping rule is used (see details). The default value is set at 1. 
nburn 
Number of burnin for HMC. The default value is set at 2000. 
niter 
Number of iterations for HMC. The default value is set at 5000. 
Details
Allocation rule:

alloc_rule=1
(Riviere et al 2014): If P(toxicity probability at combination (i,j) <target
) >c_e
: among combinations in the neighborhood (1, +1), (0, +1), (+1, 0), (+1, 1), choose the combination with a higher estimated toxicity probability than the current combination and with the estimated toxicity probability closest totarget
. If P(toxicity probability at combination (i,j) >target
) > 1c_d
: among neighborhood (1, +1), (1, 0), (0, 1), (+1, 1), choose the combination with a lower estimated toxicity probability than the current combination and with the estimated toxicity probability closest totarget
. Otherwise, remain on the same combination. 
alloc_rule=2
: Among combinations already tested and combinations in the neighborhood (1, 0), (1, +1), (0, +1), (+1, 0), (+1, 1), (0, 1), (1, 1) of a combination tested, choose the combination with the highest posterior probability to be in the targeted interval [target_min
,target_max
] while controling overdosing i.e. P(toxicity probability at combination (i,j) >target_max
) <c_over
. 
alloc_rule=3
: Among combinations in the neighborhood (1, 0), (1, +1), (0, +1), (+1, 0), (+1, 1), (0, 1), (1, 1) of the current combination, choose the combination with the highest posterior probability to be in the targeted interval [target_min
,target_max
] while controling overdosing i.e. P(toxicity probability at combination (i,j) >target_max
) <c_over
.
Early stopping for overdosing:
If the current combination is the lowest (1, 1) and at least cmin_overunder
cohorts have been included at that combination and P(toxicity probability at combination (i,j) > target
) >= c_stop
then stop the trial and do not recommend any combination.
Early stopping for underdosing:
If the current combination is the highest and at least cmin_overunder
cohorts have been included at that combination and P(toxicity probability at combination (i,j) < target
) >= c_stop
then stop the trial and do not recommend any combination.
Early stopping for identifying the MTD:

early_stop=1
(Riviere et al 2014): No stopping rule, include patients until maximum sample size is reached. 
early_stop=2
: If the next recommended combination has been tested on at leastcmin_mtd
cohorts and has a posterior probability to be in the targeted interval [target_min
,target_max
] that is >=c_t
and also control overdosing i.e. P(toxicity probability at current combination >target_max
) <c_over
then stop the trial and recommend this combination. 
early_stop=3
: If at leastcmin_mtd
cohorts have been included at the next recommended combination then stop the trial and recommend this combination.
Stopping at the maximum sample size:
If the maximum sample size is reached and no stopping rule is met, then the recommended combination is the one that was tested on at least cmin_recom
cohorts and with the highest posterior probability to be in the targeted interval [target_min
, target_max
].
Value
An object of class "CombIncrease_next" is returned, consisting of determination of the next combination and estimations. Objects generated by CombIncrease_next
contain at least the following components:
n_pat_comb 
Number of patients per combination. 
n_tox_comb 
Number of observed toxicities per combination. 
pi 
Estimated toxicity probabilities (if the startup ended). 
ptox_inf 
Estimated probabilities that the toxicity probability is inferior to 
ptox_inf_targ 
Estimated probabilities of underdosing, i.e. to be inferior to 
ptox_targ 
Estimated probabilities to be in the targeted interval [ 
ptox_sup_targ 
Estimated probabilities of overdosing, i.e. to be superior to 
(cdose1 , cdose2) 
NEXT RECOMMENDED COMBINATION. 
inconc 
Boolean indicating if trial must stop for under/over dosing. 
early_conc 
Boolean indicating if trial can be stopped earlier for finding the MTD. 
Author(s)
JacquesHenri Jourdan and MarieKarelle RiviereJourdan eldamjh@gmail.com
References
Riviere, MK., Yuan, Y., Dubois, F., and Zohar, S. (2014). A Bayesian dosefinding design for drug combination clinical trials based on the logistic model. Pharmaceutical Statistics.
See Also
Examples
prior_a1 = c(0.12, 0.2, 0.3, 0.4, 0.5)
prior_a2 = c(0.2, 0.3, 0.4)
toxicity1 = c(0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1)
dose1 = c(1,1,1,2,2,2,3,3,3,3,3,3,3,3,3,4,4,4)
dose2 = c(1,1,1,2,2,2,3,3,3,2,2,2,1,1,1,1,1,1)
t_tox = c(rep(+Inf,8),2.9,+Inf,4.6,+Inf,+Inf,+Inf,+Inf,+Inf,+Inf,5.2)
follow = c(rep(6,15), 4.9, 3.1, 1.3)
next1 = CombIncrease_next(ndose_a1=5, ndose_a2=3, target=0.3,
target_min=0.2, target_max=0.4, prior_tox_a1=prior_a1,
prior_tox_a2=prior_a2, cohort=3, final=FALSE, pat_incl=18,
dose_adm1=dose1, dose_adm2=dose2, toxicity=toxicity1, c_over=1,
cmin_overunder=3, cmin_recom=1, early_stop=1, alloc_rule=1)
next1
next2 = CombIncrease_next(ndose_a1=5, ndose_a2=3, target=0.3,
target_min=0.2, target_max=0.4, prior_tox_a1=prior_a1, prior_tox_a2=prior_a2,
cohort=3, final=FALSE, pat_incl=18, dose_adm1=dose1,
dose_adm2=dose2, tite=TRUE, time_full=6, time_tox=t_tox,
time_follow=follow, c_over=1, cmin_overunder=3, cmin_recom=1,
early_stop=1, alloc_rule=1)
next2